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Special Edition, September 2013

Should in-memory analysis have a seat at your big data table?

In-memory processing can serve as a high-octane fuel for supercharging big data analytics applications. But organizations should weigh factors such as additional systems infrastructure costs and the readiness of their business processes before gassing up with in-memory analytics technology. Another key step in greasing the deployment skids is identifying big data analytics problems that have proven unsolvable or that could benefit from the performance boost typically provided by in-memory analysis applications. "The integration of in-memory capabilities and big data boils down to use case and benefits," said Paul Barth, co-founder of data management and analytics consultancy NewVantage Partners. "You need to consider the business value of accelerating time to answer -- is it a matter of convenience, or is it a case when rapid turnaround and rapid analysis really benefits the decision-making process." Detecting patterns in large stockpiles of data is one application where using in-memory analytics tools makes sense, Barth said, ...

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